Human annotators were used to provide the category labels for Objaverse-LVIS as described in Section 3. This task was accomplished by first creating sets of 500 candidate objects for each LVIS category. These candidate sets included objects visually resembling the target category (as ranked by the CLIP features of their thumbnail images), as well as instances whose metadata contained terms with a high similarity to the target category (as ranked by their GloVe vector similarity [1]}). Candidate objects were shown to crowdworkers nine at a time, and they were asked to mark objects that were members of the category, as shown in Figure REF  a. In addition to the visual reference for each object, annotators also had access to the object's name and were encouraged to use this when helpful. Human annotators were also used to rate the relative diversity of of 3D objects generated by models trained using Objaverse and ShapeNet. The user interface and instructions for this task are shown in Figure REF  b. Two sets of nine objects generated by each model were shown with random left-right orientations, and workers were asked to choose the set exhibiting the greater variety in appearance.
<FIGURE>